1. Field of the Invention
The present invention relates generally to systems and methods for information sharing and knowledge management, and more particularly for profiling clients within a system for harvesting community knowledge.
2. Discussion of Background Art
Satisfying information needs in a diverse, heterogeneous information environment is challenging. In order to even begin the process of finding information resources or answers to questions, individuals typically must know either where to look, or whom to ask. This is often a daunting task, especially in large enterprises where many of the members will not know each other, nor be aware of all the information resources potentially at their disposal.
Current systems for storing information and/or organizational expertise include Knowledge Databases (K-bases), such as document repositories and corporate directories, and Knowledge Management systems, which rely on users to explicitly describe their personal information, knowledge, and expertise to a centralized K-base.
FIG. 1 is a dataflow diagram of a conventional knowledge management system 100. In a typical architecture, information providing users 102 explicitly decide what descriptive information they provide to a central database 104. An information seeking user 106 then performs a query on the central database 104 in order to find an information provider who perhaps may be able to answer the seeker's question.
There are several significant problems with such systems. Knowledge management systems, like that shown in FIG. 1, require that information providers spend a significant amount of time and effort entering and updating their personal information on the central database 104. For this reasons alone, such systems tend to have very low participation rates. In addition, even those information providers, who take time to enter and update this information, may misrepresent their personal information or level of knowledge and expertise be it willfully or not. Furthermore, they may neglect or be unable to reveal much of their tacit knowledge within their personal description. Tacit knowledge is knowledge a user possesses, but which the user either does not consider important enough to enter, or which they may not even be consciously aware that they know.
Because of the inaccuracy and/or incompleteness of such personal information, information seekers, even after all of their searching efforts, may still find their questions left unanswered, perhaps because the “expert” they identified may not have the bandwidth to respond. Similarly, even information seekers who discover the existence of a relevant K-base may be required to formulate queries which are so complex that they either can not or will not bother to perform a proper search
A second significant problem with knowledge management systems is the information provider's lack of privacy with respect to their personal information stored on the central database 104. No matter what agreements a knowledge management system's central database 104 provider has made with the user, the fact remains that the central database 104 provider still has the user's personal information, which means that that personal information is out of the direct control of said user. As a result, information providers may be unwilling to reveal much about themselves in the presence of a risk that their privacy would be violated. In such systems, the provider must pre-screen all information to be revealed, in order to make sure that the information provided does not contain information which the user would not be comfortable with others having access to. The resulting high participation costs often results in profiles that are stale and lack richness.
Another problem with such systems, is their lack of anonymity. Information seekers and providers cannot remain anonymous while performing queries or asking questions. As such, they may not perform a search, as a question, or wholeheartedly reveal their knowledge about a particular topic in their response to another user's question.
All of the above problems lead to free-riding by many of those using such conventional knowledge management systems. Free-riding occurs when there are information seekers who are not also information providers. They benefit from the information stored on databases, but do not contribute to them. Free-riding tends to make all users worse off, since a knowledge management system's and K-base's value depends upon the richness and fidelity of each users' contributions.
A fourth problem is cost. Conventional centralized systems require the installation of additional hardware dedicated to the knowledge management system and do not make use of otherwise unutilized resources such as the user's own personal computer.
Collaborative filtering techniques also have similar problems. Collaborative filtering is a tool for selectively presenting users with information recommendations based on the collective wisdom of the participant users. Generally these systems require users to actively mark incoming information as relevant or not relevant to their interests. A central system manages this information and attempts to group individuals with similar interests (as expressed by the ratings they assign to pieces of information). Users who seek knowledge in are then directed to information that members like them have indicated as relevant. Due to their centralized nature, these systems lack many privacy features and require heavy active participation by individuals. For this reason collaborative filtering systems frequently do not have access to rich profiles. Additionally, the information that is filtered may not address specific information needs and the user must then wade through the information or perform additional searches and may still find no answer.
In response to the concerns discussed above, what is needed is a system and method for profiling clients within a system for harvesting community knowledge that overcomes the problems of the prior art.